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Equine veterinary journal2026; doi: 10.1002/evj.70154

Transcriptomic signatures reveal systemic adaptations and immune modulation in response to training and competitive racing in horses.

Abstract: The molecular mechanisms underlying adaptation to physical exertion and racing stress in horses remain incompletely understood. Peripheral blood transcriptomics offers a minimally invasive method to monitor systemic responses to exercise and identify biomarkers of adaptation or overload. Objective: To evaluate transcriptomic changes in peripheral blood of racehorses during different phases of training and competition and to identify molecular markers of physiological adaptation and race-induced stress. Methods: Prospective transcriptomic profiling of trained racehorses across three exercise conditions. Methods: Forty racehorses (29 Arabian, 11 Thoroughbred) were sampled before (p0) and after (p1) exercise at three stages: initial training (T1), mid-season training (T2) and racing (R). RNA-seq was performed, followed by differential expression analysis (DESeq2) and pathway enrichment (g:Profiler, DAVID). Identified differentially expressed genes were integrated into STRING protein-protein interaction condition-exclusive subnetworks (Merge tool in Cytoscape) to compare the transcriptomes between conditions. Results: Distinct molecular programmes were identified at each stage. At T1, immediate-early response genes such as FOS, FOSB and HSPA6 were strongly up-regulated, reflecting acute stress and immune activation. At T2, immune-related transcripts (KLRD1, CCL4, PRF1) remained enriched, but genes linked to remodelling and adaptation, including DNAJA1, HSPH1 and CXCR4, became prominent, suggesting a shift toward recovery and regulatory processes. Post-race, chemokines such as CCL5 and stress markers (HSP90 family, JUN) were highly induced, accompanied by widespread transcriptomic divergence and down-regulation of certain immune regulators (IL18, ARHGAP44), indicating both heightened innate activation and transient immune suppression. Conclusions: Transcriptomic profiling was limited to peripheral blood, which may not reflect tissue-specific responses. Only three sampling points were included, potentially overlooking transient transcriptomic changes. Conclusions: Transcriptomic dynamics in blood reflect the transition from early immune activation (T1), through adaptation (T2), to stress-related activation post-race (R). This approach offers promising molecular biomarkers for monitoring training adaptation and detecting physiological overload in equine athletes. Unassigned: Die molekularen Mechanismen, die der Anpassung an körperliche Anstrengung und Rennstress bei Pferden zugrunde liegen, sind noch nicht vollständig geklärt. Die Transkriptomik des peripheren Blutes bietet eine minimalinvasive Methode zur Überwachung systemischer Reaktionen auf körperliche Belastung und zur Identifizierung von Biomarkern für Anpassung oder Überlastung. Unassigned: Bewertung der transkriptomischen Veränderungen im peripheren Blut von Rennpferden während verschiedener Trainings‐ und Wettkampfphasen und Identifizierung molekularer Marker für physiologische Anpassung und rennbedingten Stress. Methods: Prospektive Transkriptom‐Profilierung von trainierten Rennpferden unter drei Trainingsbedingungen. Methods: Vierzig Rennpferde (29 Araber, 11 Vollblüter) wurden vor (p0) und nach (p1) dem Training in drei Phasen untersucht: zu Beginn des Trainings (T1), in der Mitte der Saison (T2) und während des Rennens (R). Es wurde eine RNA‐Sequenzierung durchgeführt, gefolgt von einer Differentialexpressionsanalyse (DESeq2) und einer Signalweg‐Anreicherung (g:Profiler, DAVID). Identifizierte DEGs wurden in STRING PPIs bedingungsspezifische Subnetzwerke integriert (Merge‐Tool in Cytoscape), um die Transkriptome zwischen den Bedingungen zu vergleichen. Unassigned: In jeder Phase wurden unterschiedliche molekulare Programme identifiziert. Bei T1 waren sofortige Frühreaktionsgene wie FOS, FOSB und HSPA6 stark hochreguliert, was auf akuten Stress und eine Immunaktivierung hindeutet. Bei T2 blieben immunbezogene Transkripte (KLRD1, CCL4, PRF1) angereichert, aber Gene, die mit Umbau und Anpassung in Verbindung stehen, darunter DNAJA1, HSPH1 und CXCR4, traten in den Vordergrund, was auf eine Verlagerung hin zu Erholungs‐ und Regulationsprozessen hindeutet. Nach dem Rennen waren Chemokine wie CCL5 und Stressmarker (HSP90‐Familie, JUN) stark induziert, begleitet von einer weitreichenden transkriptomischen Divergenz und einer Herunterregulierung bestimmter Immunregulatoren (IL18, ARHGAP44), was sowohl auf eine verstärkte angeborene Aktivierung als auch auf eine vorübergehende Immunsuppression hindeutet. Unassigned: Die Transkriptom‐Profilierung beschränkte sich auf peripheres Blut, was möglicherweise nicht die gewebespezifischen Reaktionen widerspiegelt. Es wurden nur drei Probenentnahmepunkte berücksichtigt, wodurch möglicherweise vorübergehende transkriptomische Veränderungen übersehen wurden. Unassigned: Die Transkriptomdynamik im Blut spiegelt den Übergang von der frühen Immunaktivierung (T1) über die Anpassung (T2) bis hin zur stressbedingten Aktivierung nach dem Rennen (R) wider. Dieser Ansatz bietet vielversprechende molekulare Biomarker für die Überwachung der Trainingsanpassung und die Erkennung physiologischer Überlastung bei Sportpferden.
© 2026 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2026-03-07 PubMed ID: 41793277DOI: 10.1002/evj.70154Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study examined how gene expression in the blood of racehorses changes during different training phases and competitive racing, aiming to identify molecular markers that reflect physiological adaptation and stress.
  • Researchers used RNA sequencing to capture transcriptomic profiles before and after exercise at three key stages, revealing distinct immune and stress response patterns linked to training adaptation and race-induced strain.

Background and Objective

  • The biological mechanisms by which horses adapt to physical exertion and the stress of racing are not fully understood.
  • Peripheral blood transcriptomics — analyzing RNA from blood samples — serves as a minimally invasive method to monitor systemic responses to exercise.
  • This approach helps identify biomarkers indicating whether a horse is adapting well or experiencing overload from training and racing stress.
  • The main goal was to evaluate changes in gene expression in racehorses’ blood during different training phases and competitive racing to pinpoint molecular markers of physiological adaptation and stress.

Methods

  • Forty trained racehorses were studied, including 29 Arabian and 11 Thoroughbred horses.
  • Blood samples were taken before (p0) and after (p1) exercise at three stages:
    • Initial training phase (T1)
    • Mid-season training phase (T2)
    • During actual racing events (R)
  • RNA sequencing (RNA-seq) was performed on these samples to profile gene expression.
  • Differential expression analysis was conducted using the DESeq2 tool to identify genes whose expression levels changed significantly between conditions.
  • Pathway enrichment analysis using tools like g:Profiler and DAVID identified biological processes and pathways associated with these genes.
  • Identified differentially expressed genes were integrated into condition-specific protein-protein interaction networks using STRING and visualized with Cytoscape’s Merge tool to compare transcriptomic profiles across the three conditions.

Key Findings

  • Initial Training (T1):
    • Strong up-regulation of immediate-early response genes (e.g., FOS, FOSB, HSPA6) indicating acute stress and immune system activation immediately following exercise.
  • Mid-Season Training (T2):
    • Immune-related transcripts such as KLRD1, CCL4, and PRF1 remained elevated, showing ongoing immune involvement.
    • Genes linked to tissue remodeling and adaptation—including DNAJA1, HSPH1, and CXCR4—became more prominent, suggesting a shift toward recovery, repair, and regulatory processes.
  • Post-Race (R):
    • Increased expression of chemokines like CCL5 and stress markers belonging to the HSP90 family and JUN indicated heightened stress response.
    • There was widespread divergence in gene expression profiles compared to training phases.
    • Some immune regulators (e.g., IL18, ARHGAP44) were down-regulated, suggesting transient immune suppression following intense exertion.
    • This combination points to both increased innate immune activation and a temporary dampening of parts of the immune system post-race.

Interpretation and Conclusions

  • Peripheral blood transcriptomics captures systemic adaptations of the immune system over a horse’s training cycle:
    • An initial acute immune activation immediately after beginning training.
    • A transitional phase focusing on recovery, tissue remodeling, and regulated immune activity as training progresses.
    • Strong stress responses and shifts in immune balance following race exertion.
  • The study highlights promising molecular biomarkers that could be used to monitor whether horses are adapting well or are at risk of physiological overload during training and racing.
  • Limitations include restricting analysis to peripheral blood, which might not fully represent gene expression dynamics in muscle or other tissues directly involved in exertion.
  • Only three sampling points were assessed, potentially missing more transient or immediate gene expression changes that occur during or shortly after exercise.
  • Future studies could incorporate more time points and tissue types to build a comprehensive picture of the molecular processes underlying equine athletic performance and recovery.

Significance

  • This research provides a molecular framework to understand how systemic immune and stress responses evolve throughout a racehorse’s training and competitive season.
  • It supports the use of blood-based transcriptomic biomarkers for non-invasive monitoring of horse health, training adaptation, and early detection of overtraining or stress-induced immune suppression.
  • Such insights can ultimately contribute to better management and welfare of equine athletes by guiding training regimens and recovery strategies based on molecular evidence.

Cite This Article

APA
Dąbrowska I, Grzędzicka-Agko J, Kiełbik P, Trela M, Witkowska-Piłaszewicz O. (2026). Transcriptomic signatures reveal systemic adaptations and immune modulation in response to training and competitive racing in horses. Equine Vet J. https://doi.org/10.1002/evj.70154

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English

Researcher Affiliations

Dąbrowska, Izabela
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Grzędzicka-Agko, Jowita
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Kiełbik, Paula
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Trela, Michał
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.
Witkowska-Piłaszewicz, Olga
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland.

Grant Funding

  • 2021/41/B/NZ7/03548 / Narodowe Centrum Nauki
  • Science Development Fund of the Warsaw University of Life Sciences-SGGW

References

This article includes 84 references
  1. Chanda M, Petchdee S. Cardiac morphology changes in horses as a response to various types of sports. J Appl Anim Res 2022;50(1):453–459.
    doi: 10.1080/09712119.2022.2097245google scholar: lookup
  2. Plisak U, Szczepaniak J, Żmigrodzka M, Giercuszkiewicz‐Hecold B, Witkowska‐Piłaszewicz O. Changes in novel anti‐inflammatory cytokine concentration in the blood of endurance and race horses at different levels of training. Comput Struct Biotechnol J 2023;21:418–424.
    doi: 10.1016/j.csbj.2022.12.016google scholar: lookup
  3. te Pas MFW, Wijnberg ID, Hoekman AJW, de Graaf‐Roelfsema E, Keizer HA, van Breda E. Skeletal muscle transcriptome profiles related to different training intensities and detraining in standardbred horses: a search for overtraining biomarkers. Vet J 2013;197(3):717–723.
    doi: 10.1016/j.tvjl.2013.03.052google scholar: lookup
  4. Vermeulen R, De Meeûs C, Plancke L, Boshuizen B, De Bruijn M, Delesalle C. Fysiologische effecten van verschillende soorten arbeid op de spierontwikkeling van het paard. Vlaams Diergeneeskd Tijdschr 2017;86(4):224–230.
    doi: 10.21825/vdt.v86i4.16183google scholar: lookup
  5. Wilson J, De Donato M, Appelbaum B, Garcia CT, Peters S. Differential expression of innate and adaptive immune genes during acute physical exercise in American quarter horses. Animals 2023;13(2):308.
    doi: 10.3390/ani13020308google scholar: lookup
  6. Piccione G, Fazio F, Giannetto C, Assenza A, Caola G. Oxidative stress in Thoroughbreds during official 1800‐metre races. Vet Arch 2007;77(3):219–227.
  7. Esser MM, McKenzie EC, Payton ME. Serum immunoglobulin concentrations in horses racing a multiday endurance event. Comp Exerc Physiol 2014;10(3):173–179.
    doi: 10.3920/cep140009google scholar: lookup
  8. Klobučar K, Vrbanac Z, Gotić J, Bojanić K, Bureš T, Bottegaro NB. Changes in biochemical parameters in horses during 40 km and 80 km endurance races. Acta Vet Brno 2019;69(1):73–87.
    doi: 10.2478/acve-2019-0005google scholar: lookup
  9. Hyyppä S. Endocrinal responses in exercising horses. Livest Prod Sci 2005;92:113–121.
    doi: 10.1016/j.livprodsci.2004.11.014google scholar: lookup
  10. Purvis D, Gonsalves S, Deuster PA. Physiological and psychological fatigue in extreme conditions: overtraining and elite athletes. PM&R 2010;2(5):442–450.
    doi: 10.1016/j.pmrj.2010.03.025google scholar: lookup
  11. Pillon NJ, Gabriel BM, Dollet L, Smith JAB, Sardón Puig L, Botella J. Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity. Nat Commun 2020;11(1):470.
    doi: 10.1038/s41467-019-13869-wgoogle scholar: lookup
  12. Stefaniuk‐Szmukier M, Szmatoła T, Ropka‐Molik K. Molecular signatures of exercise adaptation in Arabian racing horses: transcriptomic insights from blood and muscle. Genes 2025;16(4):431.
    doi: 10.3390/genes16040431google scholar: lookup
  13. Hill EW, Gu J, Eivers SS, Fonseca RG, McGivney BA, Govindarajan P. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in Thoroughbred horses. PLoS One 2010;5(1):8645.
    doi: 10.1371/journal.pone.0008645google scholar: lookup
  14. McGivney BA, McGettigan PA, Browne JA, Evans ACO, Fonseca RG, Loftus BJ. Characterization of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training. BMC Genomics 2010;11(1):398.
    doi: 10.1186/1471-2164-11-398google scholar: lookup
  15. Ebisuda Y, Mukai K, Takahashi Y, Yoshida T, Kawano A, Matsuhashi T. Acute exercise in a hot environment increases heat shock protein 70 and peroxisome proliferator‐activated receptor γ coactivator 1α mRNA in Thoroughbred horse skeletal muscle. Front Vet Sci 2023;10:1230212.
    doi: 10.3389/fvets.2023.1230212google scholar: lookup
  16. Ropka‐Molik K, Stefaniuk‐Szmukier M, Żukowski K, Piórkowska K, Gurgul A, Bugno‐Poniewierska M. Transcriptome profiling of Arabian horse blood during training regimens. BMC Genet 2017;18(1):31.
    doi: 10.1186/s12863-017-0499-1google scholar: lookup
  17. Bonometti S, Menarim BC, Reinholt BM, Ealy AD, Johnson SE. Growth factor modulation of equine trophoblast mitosis and prostaglandin gene Expression1. J Anim Sci 2019;97(2):865–873.
    doi: 10.1093/jas/sky473google scholar: lookup
  18. Martano M, Power K, Restucci B, Pagano I, Altamura G, Borzacchiello G. Expression of vascular endothelial growth factor (VEGF) in equine sarcoid. BMC Vet Res 2018;14(1):266.
    doi: 10.1186/s12917-018-1576-zgoogle scholar: lookup
  19. Akçay S, Gurkok‐Tan T, Ekici S. Identification of key genes in immune‐response post‐endurance run in horses. J Equine Vet Sci 2025;149:105418.
    doi: 10.1016/j.jevs.2025.105418google scholar: lookup
  20. Capomaccio S, Vitulo N, Verini‐Supplizi A, Barcaccia G, Albiero A, D'Angelo M. RNA sequencing of the exercise transcriptome in equine athletes. PLoS One 2013;8(12):83504.
    doi: 10.1371/journal.pone.0083504google scholar: lookup
  21. Takahashi K, Mukai K, Takahashi Y, Ebisuda Y, Hatta H, Kitaoka Y. Comparison of long‐ and short‐rest periods during high‐intensity interval exercise on transcriptomic responses in equine skeletal muscle. Physiol Genomics 2025;57(1):28–39.
    doi: 10.1152/physiolgenomics.00066.2024google scholar: lookup
  22. Page AE, Adam E, Arthur R, Barker V, Franklin F, Friedman R. Expression of select mRNA in Thoroughbreds with catastrophic racing injuries. Equine Vet J 2022;54(1):63–73.
    doi: 10.1111/evj.13423google scholar: lookup
  23. Tozaki T, Kikuchi M, Kakoi H, Hirota KI, Mukai K, Aida H. Profiling of exercise‐induced transcripts in the peripheral blood cells of Thoroughbred horses. J Equine Sci 2016;27(4):157–164.
    doi: 10.1294/jes.27.157google scholar: lookup
  24. Khummuang S, Lee HG, Joo SS, Park JW, Choi JY, Oh JH. Comparison for immunophysiological responses of Jeju and Thoroughbred horses after exercise. Asian Australas J Anim Sci 2019;33(3):424–435.
    doi: 10.5713/ajas.19.0260google scholar: lookup
  25. Rose R. Haematological changes associated with endurance exercise. Vet Rec 1982;110(8):175–177.
    doi: 10.1136/vr.110.8.175google scholar: lookup
  26. Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA‐seq read counts. Genome Biol 2014;15(2):R29.
    doi: 10.1186/gb-2014-15-2-r29google scholar: lookup
  27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13(11):2498–2504.
    doi: 10.1101/gr.1239303google scholar: lookup
  28. Smith JE, Erickson HH, Debowes RM, Clark M. Changes in circulating equine erythrocytes induced by brief, high‐speed exercise. Equine Vet J 1989;21(6):444–446.
    doi: 10.1111/j.2042-3306.1989.tb02193.xgoogle scholar: lookup
  29. Boucher JH, Ferguson EW, Wilhelmsen CL, Statham N, McMeekin RR. Erythrocyte alterations endurance exercise in horses. J Appl Physiol 1981;51(1):131–134.
    doi: 10.1152/jappl.1981.51.1.131google scholar: lookup
  30. Baskurt OK, Farley RA, Meiselman HJ. Erythrocyte aggregation tendency and cellular properties in horse, human, and rat: a comparative study. Am J Physiol Heart Circ Physiol 1997;273(6):H2604–H2612.
    doi: 10.1152/ajpheart.1997.273.6.h2604google scholar: lookup
  31. Hanzawa K, Watanabe S. Changes in osmotic fragility of erythrocytes during exercise in athletic horses.. J Equine Sci 2000;11(3):51–61.
    doi: 10.1294/jes.11.51google scholar: lookup
  32. Cappelli K, Felicetti M, Capomaccio S, Pieramati C, Silvestrelli M, Verini‐Supplizi A. Exercise‐induced up‐regulation of MMP‐1 and IL‐8 genes in endurance horses.. BMC Physiol 2009;9(1):12.
    doi: 10.1186/1472-6793-9-12google scholar: lookup
  33. Lee HG, Choi JY, Park JW, Park TS, Song KD, Shin D. Effects of exercise on myokine gene expression in horse skeletal muscles.. Asian‐Australas J Anim Sci 2019;32(3):350–356.
    doi: 10.5713/ajas.18.0375google scholar: lookup
  34. Murcia RY, Vargas A, Lavoie JP. The interleukin‐17 induced activation and increased survival of equine neutrophils is insensitive to glucocorticoids.. PLoS One 2016;11(5):e0154755.
    doi: 10.1371/journal.pone.0154755google scholar: lookup
  35. McGivney BA, Eivers SS, MacHugh DE, MacLeod JN, O'Gorman GM, Park SDE. Transcriptional adaptations following exercise in Thoroughbred horse skeletal muscle highlights molecular mechanisms that lead to muscle hypertrophy.. BMC Genomics 2009;10(1):638.
    doi: 10.1186/1471-2164-10-638google scholar: lookup
  36. Pernow J, Öhlén A, Hökfelt T, Nilsson O, Lundberg JM. Neuropeptide Y: presence in perivascular noradrenergic neurons and vasoconstrictor effects on skeletal muscle blood vessels in experimental animals and man.. Regul Pept 1987;19(5‐6):313–324.
    doi: 10.1016/0167-0115(87)90173-xgoogle scholar: lookup
  37. Puntschart A, Wey E, Jostarndt K, Vogt M, Wittwer M, Widmer HR. Expression of fos and jun genes in human skeletal muscle after exercise.. Am J Physiol 1998;274(1):C129–C137.
    doi: 10.1152/ajpcell.1998.274.1.c129google scholar: lookup
  38. Patel RS, Tomlinson JE, Divers TJ, Van De Walle GR, Rosenberg BR. Single‐cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T‐bet+ B cells.. BMC Biol 2021;19(1):13.
    doi: 10.1186/s12915-020-00947-5google scholar: lookup
  39. Riihimäki M, Fegraeus K, Nordlund J, Waern I, Wernersson S, Akula S. Single‐cell transcriptomics delineates the immune cell landscape in equine lower airways and reveals upregulation of FKBP5 in horses with asthma.. Sci Rep 2023;13(1):16261.
    doi: 10.1038/s41598-023-43368-4google scholar: lookup
  40. Witkowska‐Piłaszewicz O, Pingwara R, Winnicka A. The effect of physical training on peripheral blood mononuclear cell ex vivo proliferation, differentiation, activity, and reactive oxygen species production in racehorses.. Antioxidants 2020;9(11):1155.
    doi: 10.3390/antiox9111155google scholar: lookup
  41. Park KD, Park J, Ko J, Kim BC, Kim HS, Ahn K. Whole transcriptome analyses of six Thoroughbred horses before and after exercise using RNA‐seq.. BMC Genomics 2012;13(1):473.
    doi: 10.1186/1471-2164-13-473google scholar: lookup
  42. Pakula PD, Halama A, Al‐Dous EK, Johnson SJ, Filho SA, Suhre K. Characterization of exercise‐induced hemolysis in endurance horses.. Front Vet Sci 2023;10:1115776.
    doi: 10.3389/fvets.2023.1115776google scholar: lookup
  43. Plasenzotti R, Stoiber B, Posch M, Windberger U. Red blood cell deformability and aggregation behaviour in different animal species.. Clin Hemorheol Microcirc 2004;31(2):105–111.
  44. Ropka‐Molik K, Stefaniuk‐Szmukier M, Żukowski K, Piórkowska K, Bugno‐Poniewierska M. Exercise‐induced modification of the skeletal muscle transcriptome in Arabian horses.. Physiol Genomics 2017;49(6):318–326.
    doi: 10.1152/physiolgenomics.00130.2016google scholar: lookup
  45. Kitaoka Y, Mukai K, Tonai S, Ohmura H, Takahashi T. Effect of post‐exercise muscle cooling on PGC‐1α and VEGF mRNA expression in Thoroughbreds.. Comp Exerc Physiol 2022;18(1):21–26.
    doi: 10.3920/cep210006google scholar: lookup
  46. Hiscock N, Fischer CP, Pilegaard H, Pedersen BK. Vascular endothelial growth factor mRNA expression and arteriovenous balance in response to prolonged, submaximal exercise in humans.. Am J Physiol Heart Circ Physiol 2003;285(4):H1759–H1763.
    doi: 10.1152/ajpheart.00150.2003google scholar: lookup
  47. Song KD, Cho HW, Lee HK, Cho BW. Molecular characterization and expression analysis of equine vascular endothelial growth factor alpha (VEGFα) gene in horse (Equus caballus).. Asian Australas J Anim Sci 2014;27(5):743–748.
    doi: 10.5713/ajas.2013.13821google scholar: lookup
  48. Kovac M, Berestov IV, Aimaletdinov AM, Rutland CS, Rizvanov AA, Zakirova EY. Gene therapy using plasmid DNA encoding bone morphogenetic protein 2 and vascular endothelial growth factor 164 genes for the treatment of equine proximal suspensory desmitis: case reports.. OM&P 2024;11(2):74–89.
    doi: 10.24412/2500-2295-2024-2-74-89google scholar: lookup
  49. LaVigne EK, Jones AK, Londoño AS, Schauer AS, Patterson DF, Nadeau JA. Muscle growth in young horses: effects of age, cytokines, and growth factors1.. J Anim Sci 2015;93(12):5672–5680.
    doi: 10.2527/jas.2015-9634google scholar: lookup
  50. Kim JW, Bae JH, Go GY, Lee JR, Jeong Y, Kim JY. Epsti1 regulates the inflammatory stage of early muscle regeneration through STAT1‐VCP interaction.. Int J Biol Sci 2024;20(9):3530–3543.
    doi: 10.7150/ijbs.94675google scholar: lookup
  51. Trenerry MK, Della Gatta PA, Larsen AE, Garnham AP, Cameron‐Smith D. Impact of resistance exercise training on interleukin‐6 and JAK/STAT in young men.. Muscle Nerve 2011;43(3):385–392.
    doi: 10.1002/mus.21875google scholar: lookup
  52. Sajiir H, Ramm GA, Macdonald GA, McGuckin MA, Prins JB, Hasnain SZ. Harnessing IL‐22 for metabolic health: promise and pitfalls.. Trends Mol Med 2025;31(6):574–584.
    doi: 10.1016/j.molmed.2024.10.016google scholar: lookup
  53. Huang Y, Yu F, Ding Y, Zhang H, Li X, Wang X. Hepatic IL22RA1 deficiency promotes hepatic steatosis by modulating oxysterol in the liver.. Hepatology 2025;81(5):1564–1582.
    doi: 10.1097/hep.0000000000000998google scholar: lookup
  54. Kauffman AS. Emerging functions of gonadotropin‐releasing hormone II in mammalian physiology and behaviour.. J Neuroendocrinol 2004;16(9):794–806.
    doi: 10.1111/j.1365-2826.2004.01229.xgoogle scholar: lookup
  55. Burcelin R, Thorens B, Glauser M, Gaillard RC, Pralong FP. Gonadotropin‐releasing hormone secretion from hypothalamic neurons: stimulation by insulin and potentiation by leptin.. Endocrinology 2003;144(10):4484–4491.
    doi: 10.1210/en.2003-0457google scholar: lookup
  56. Stevenson TJ, Hahn TP, MacDougall‐Shackleton SA, Ball GF. Gonadotropin‐releasing hormone plasticity: a comparative perspective.. Front Neuroendocrinol 2012;33(3):287–300.
    doi: 10.1016/j.yfrne.2012.09.001google scholar: lookup
  57. Sapronova АА, Ryabushkina YA, Kisaretovа PE, Bondar NP. Mechanisms of adaptation of the hypothalamic‐pituitary‐adrenal axis in male mice under chronic social defeat stress.. Zh Vyssh Nerv Deiat Im I P Pavlova 2024;74(2):197–209.
    doi: 10.31857/s0044467724020058google scholar: lookup
  58. Kuizon E, Pearce EG, Bailey SG, Chen‐Scarabelli C, Yuan Z, Abounit K. Mechanisms of action and clinical implications of cardiac urocortin: a journey from the heart to the systemic circulation, with a stopover in the mitochondria.. Int J Cardiol 2009;137(3):189–194.
    doi: 10.1016/j.ijcard.2009.03.112google scholar: lookup
  59. Kavalakatt S, Khadir A, Madhu D, Hammad M, Devarajan S, Abubaker J. Urocortin 3 levels are impaired in overweight humans with and without type 2 diabetes and modulated by exercise.. Front Endocrinol 2019;10:762.
    doi: 10.3389/fendo.2019.00762google scholar: lookup
  60. Takahashi M, Kubota S. Exercise‐related novel gene is involved in myoblast differentiation.. Biomed Res 2005;26(2):79–85.
    doi: 10.2220/biomedres.26.79google scholar: lookup
  61. Zang D, Wang C, Ji X, Wang Y. Tamarix hispida zinc finger protein ThZFP1 participates in salt and osmotic stress tolerance by increasing proline content and SOD and POD activities.. Plant Sci 2015;235:111–121.
    doi: 10.1016/j.plantsci.2015.02.016google scholar: lookup
  62. Van Den Bergen JA, Robevska G, Eggers S, Riedl S, Grover SR, Bergman PB. Analysis of variants in GATA4 and FOG2 / ZFPM2 demonstrates benign contribution to 46,XY disorders of sex development.. Mol Genet Genomic Med 2020;8(3):e1095.
    doi: 10.1002/mgg3.1095google scholar: lookup
  63. Rabenstein RL, Addy NA, Caldarone BJ, Asaka Y, Gruenbaum LM, Peters LL. Impaired synaptic plasticity and learning in mice lacking β‐adducin, an actin‐regulating protein.. J Neurosci 2005;25(8):2138–2145.
    doi: 10.1523/jneurosci.3530-04.2005google scholar: lookup
  64. Kiang KMY, Leung GKK. A review on adducin from functional to pathological mechanisms: future direction in cancer.. Biomed Res Int 2018;2018:1–14.
    doi: 10.1155/2018/3465929google scholar: lookup
  65. Pozzi D, Corradini I, Matteoli M. The control of neuronal calcium homeostasis by SNAP‐25 and its impact on neurotransmitter release.. Neuroscience 2019;420:72–78.
    doi: 10.1016/j.neuroscience.2018.11.009google scholar: lookup
  66. Endo Y, Zhang Y, Olumi S, Karvar M, Argawal S, Neppl RL. Exercise‐induced gene expression changes in skeletal muscle of old mice.. Genomics 2021;113(5):2965–2976.
    doi: 10.1016/j.ygeno.2021.06.035google scholar: lookup
  67. Rademacher N, Schmerl B, Lardong JA, Wahl MC, Shoichet SA. MPP2 is a postsynaptic MAGUK scaffold protein that links SynCAM1 cell adhesion molecules to core components of the postsynaptic density.. Sci Rep 2016;6(1):35283.
    doi: 10.1038/srep35283google scholar: lookup
  68. Schmerl B, Gimber N, Kuropka B, Stumpf A, Rentsch J, Kunde S‐A. The synaptic scaffold protein MPP2 interacts with GABAA receptors at the periphery of the postsynaptic density of glutamatergic synapses.. PLoS Biol 2022;20(3):e3001503.
    doi: 10.1371/journal.pbio.3001503google scholar: lookup
  69. Hestand MS, Kalbfleisch TS, Coleman SJ, Zeng Z, Liu J, Orlando L. Annotation of the protein coding regions of the equine genome.. PLoS One 2015;10(6):e0124375.
    doi: 10.1371/journal.pone.0124375google scholar: lookup
  70. Zhong C, Wang H, Yuan X, He Y, Cong J, Yang R. The crucial role of HFM1 in regulating FUS ubiquitination and localization for oocyte meiosis prophase I progression in mice.. Biol Res 2024;57(1):36.
    doi: 10.1186/s40659-024-00518-wgoogle scholar: lookup
  71. Kim DH, Lee HG, Sp N, Kang DY, Jang KJ, Lee HK. Validation of exercise‐response genes in skeletal muscle cells of Thoroughbred racing horses.. Anim Biosci 2021;34(1):134–142.
    doi: 10.5713/ajas.18.0749google scholar: lookup
  72. de Barcellos LAM, Gonçalves WA, de Esteves Oliveira MP, Guimarães JB, Queiroz‐Junior CM, de Resende CB. Effect of physical training on exercise‐induced inflammation and performance in mice.. Front Cell Dev Biol 2021;9:1–10.
    doi: 10.3389/fcell.2021.625680google scholar: lookup
  73. Ahn H, Kim J, Lee H, Lee E, Lee GS. Characterization of equine inflammasomes and their regulation.. Vet Res Commun 2020;44(2):51–59.
    doi: 10.1007/s11259-020-09772-1google scholar: lookup
  74. Kang H, Lee GKC, Bienzle D, Arroyo LG, Sears W, Lillie BN. Equine alveolar macrophages and monocyte‐derived macrophages respond differently to an inflammatory stimulus.. PLoS One 2023;18(3):e0282738.
    doi: 10.1371/journal.pone.0282738google scholar: lookup
  75. Mach N, Ramayo‐Caldas Y, Clark A, Moroldo M, Robert C, Barrey E. Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses.. BMC Genomics 2017;18(1):187.
    doi: 10.1186/s12864-017-3571-3google scholar: lookup
  76. Hjorth M, Egan CL, Telles GD, Pal M, Gallego‐Ortega D, Fuller OK. Decorin, an exercise‐induced secretory protein, is associated with improved prognosis in breast cancer patients but does not mediate anti‐tumorigenic tissue crosstalk in mice.. J Sport Health Sci 2025;14:100991.
    doi: 10.1016/j.jshs.2024.100991google scholar: lookup
  77. Su Y, Ren W, Ma S, Meng J, Yao X, Zeng Y. Comparative analysis of blood whole transcriptome profiles in Yili horses pre‐ and post‐5000‐meter racing.. Front Genet 2025;16:1651628.
    doi: 10.3389/fgene.2025.1651628google scholar: lookup
  78. Wubuli A, Su Y, Yao X, Meng J, Wang J, Zeng Y. Transcriptome analysis of muscle tissue from three anatomical locations in male and female Kazakh horses.. Biology 2025;14(9):1216.
    doi: 10.3390/biology14091216google scholar: lookup
  79. Gunn HM. Differences in the histochemical properties of skeletal muscles of different breeds of horses and dogs.. J Anat 1978;127(Pt 3):615–634.
  80. Marlin DJ, Harris RC, Gash SP, Snow DH. Carnosine content of the middle gluteal muscle in Thoroughbred horses with relation to age, sex and training.. Comp Biochem Physiol A Physiol 1989;93(3):629–632.
    doi: 10.1016/0300-9629(89)90023-6google scholar: lookup
  81. Bryan K, McGivney BA, Farries G, McGettigan PA, McGivney CL, Gough KF. Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components. BMC Genomics 2017;18(1):595.
    doi: 10.1186/s12864-017-4007-9google scholar: lookup
  82. Farries G, Bryan K, McGivney CL, McGettigan P, Gough KF, Browne JA. Expression quantitative trait loci in equine skeletal muscle reveals heritable variation in metabolism and the training responsive transcriptome. Front Genet 2019;10:10.
    doi: 10.3389/fgene.2019.01215google scholar: lookup
  83. Haller N, Behringer M, Reichel T, Wahl P, Simon P, Krüger K. Blood‐based biomarkers for managing workload in athletes: considerations and recommendations for evidence‐based use of established biomarkers. Sports Med 2023;53(7):1315–1333.
    doi: 10.1007/s40279-023-01836-xgoogle scholar: lookup
  84. Cywinska A, Szarska E, Kowalska A, Ostaszewski P, Schollenberger A. Gender differences in exercise – induced intravascular haemolysis during race training in Thoroughbred horses. Res Vet Sci 2011;90(1):133–137.
    doi: 10.1016/j.rvsc.2010.05.004google scholar: lookup

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